Abstract
This paper introduces DenLAC (Density Levels Aggregation Clustering), an adaptable clustering algorithm which achieves high accuracy independent of the input’s shape and distribution. While most clustering algorithms are specialized on particular input types, DenLAC obtains correct results for spherical, elongated and different density clusters. We also incorporate a simple procedure for outlier identification and displacement. Our method relies on defining clusters as density intervals comprised of connected components which we call density bins, through assembling several popular notions in data mining and statistics such as Kernel Density Estimation, the density attraction and density levels theoretical concepts. To build the final clusters, we extract the connected components from each density bin and we merge adjacent connected components using a slightly modified agglomerative clustering algorithm.
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Rădulescu, IM., Boicea, A., Truică, CO., Apostol, ES., Mocanu, M., Rădulescu, F. (2021). DenLAC: Density Levels Aggregation Clustering – A Flexible Clustering Method –. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_27
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